Merchant review management using optimized review distribution

ABSTRACT

A system and method to optimize dynamic review generation. A user device associated with a user is selected to provide a review of a merchant. A weighted event factor associated at least a portion of a plurality of websites to which the review can be redirected is identified. Based on the weighted event factor, a first website of the plurality of websites to which to redirect the user device is determined. A dynamic redirection container is transmitted that redirects the client device to the first website, wherein the dynamic redirection container receives a posted review of the merchant at the first website. Based on the posted review associated with the merchant, the weighted event factor is updated.

CROSS REFERENCE TO RELATED APPLICATIONS

This application is a continuation application of U.S. patentapplication Ser. No. 16/515,855, filed on Jul. 18, 2019, which is adivisional application of U.S. patent application Ser. No. 15/494,122,filed Apr. 21, 2017 (issued as U.S. Pat. No. 10,417,671 on Sep. 17,2019), which in turn claims the benefit of U.S. Provisional ApplicationNo. 62/415,946, filed Nov. 1, 2016. The entire disclosures of U.S.patent application Ser. No. 16/515,855, U.S. patent application Ser. No.15/494,122, and U.S. Provisional Application No. 62/415,946 areincorporated herein by reference.

TECHNICAL FIELD

The disclosure relates generally to Internet search impressionstechnology, and more particularly, to methods and systems for optimizingdynamic review generation for redirecting request links.

BACKGROUND

On any particular review website, a review posted may appear in a searchengine result related to a particular business that is the subject ofthe review. To post a review, users typically can access numerous sitesusing the address of a specific web page on the Internet. Because of theviral nature of the Internet, the reviews and comments are generallyuser controlled and therefore wide ranging in focus and scope.

BRIEF DESCRIPTION OF THE DRAWINGS

The present disclosure is illustrated by way of example, and not by wayof limitation, and can be more fully understood with reference to thefollowing detailed description when considered in connection with thefigures as described below.

FIG. 1 illustrates an example of a high-level architecture foroptimizing dynamic review generation for redirecting request links inaccordance with one or more aspects of the disclosure.

FIG. 2 illustrates is an example system including memory for optimizingdynamic review generation for redirecting request links in accordancewith one or more aspects of the disclosure.

FIG. 3 illustrates an example interface portal to support optimizingdynamic review generation for redirecting request links in accordancewith one or more aspects of the disclosure.

FIG. 4 illustrates an example dashboard portal to support optimizingdynamic review generation for redirecting request links in accordancewith one or more aspects of the disclosure.

FIG. 5 illustrates a flow diagram of a method for optimizing dynamicreview generation for redirecting request links in accordance with oneor more aspects of the disclosure.

FIG. 6 illustrates a flow diagram of a method for modifying optimizeddynamic review generation for redirecting request links in accordancewith one or more aspects of the disclosure.

FIG. 7 illustrates a flow diagram of a method for optimizing dynamicreview generation for redirecting request links based on user logininformation in accordance with one or more aspects of the disclosure.

FIG. 8 illustrates an example computer system operating in accordancewith some embodiments of the disclosure.

DETAILED DESCRIPTION

Many businesses are increasingly concerned about their onlineimpressions. For example, both positive and negative reviews posted to areview website can impact the business. As more review websites arecreated on the Internet, and as more users post content to those sites,it is becoming increasingly difficult for businesses to monitor suchsites. Further, it can be difficult for businesses to determine whetherthey need to, and how they can, improve their online impressions todrive traffic to their locations. Additionally, businesses want toincrease the number of reviews from their customers as much as possible.Typically, only a minority of customers that visit a merchant postreviews. Businesses are motivated to get their customers to leave asmany reviews as possible, and one issue has been ensuring reviews arecoordinated in an optimal distribution across the numerous reviewingsites on the Internet.

Methods and systems for creating and distributing dynamic “reviewrequests” are disclosed herein. In one example embodiment, dynamic“review request” links are generated. Each link may dynamically re-routeconsumers to the optimal website to improve the quality of review dataacross the Internet. The link could be unbranded, or the link itselfcould be generated after the optimal review site is determined. Bygathering reviews and determining the optimal review site for a reviewbased on several event factors related to a cross section of thereviewing sites, the techniques of the disclosure may ensure that thereviews reflect the most current consumer sentiment about the business.Advantages include: 1) ensuring that there are accurate average ratingsacross sites; 2) that there are an optimal number of reviews; and 3)that the reviews are recent. An additional advantage of this is thatreviews may be distributed in a way to ensure that a business' onlineimpression is consistent across the Internet.

In some implementations, a source system creates a dynamic redirectioncontainer, such as a dynamic Uniform Resource Locator (URL) link, thatis then transmitted to a user's device to request a review of a businesslocation. For example, the dynamic redirection container may be, but notlimited to a computer readable object comprising dynamic URL, textmessage or other types of controls for redirecting the display of auser's client device to a particular Internet resource. The request maybe sent on behalf of a merchant in response to detecting a conditionassociated with the user. These conditions may include the userentering/leaving the merchant's location, a purchase by the user relatedto the merchant, the browsing history of the user, or other types ofconditions.

An existing distribution of reviews for the business across a pluralityof review sites is monitored. For example, an apparatus (e.g., a webcrawler) may be used to crawl through review and rating sites on theInternet. In another embodiment, the access may be through one or moreAPIs, but also may be through scraping, JavaScript or any other methodof pulling data from such sites. The analytics and data associated withthese pre-existing reviews may be used as part of the algorithmimplemented in the dynamic review optimizer described below.

The apparatus accesses data from the web and stores this data in adatabase. In some implementations, review data may be accessed from asource system associated with the merchant. If the user decides toprovide a review of the merchant by selecting the dynamic redirectioncontainer, a determination is made as to whether the existingdistribution of reviews across the review sites correctly quantifiescurrent consumer sentiment about the business. For example, based onseveral weighted event factors, the user is redirected to a review siteto optimize the benefit of that review to the merchant.

In some implementations, once a review is generated through the dynamicredirection container that review may be pulled back into the sourcesystem so that it can be monitored with other review currently beingmonitored. For example, the reviews are “flagged” or otherwise tagged toindicate that the reviews originated from the dynamic review generationdynamic redirection container. The source system can then eitherautomatically or manually update the dynamic review generation algorithmbased on how the monitored reviews are meeting the desired optimizationparameters specified by the merchant via the weighted event factors.

In alternative implementations, the source system may detect if thereviewer is logged into a particular reviewing site. At this point, thedynamic redirection container may use the weighted event factors toselect sites with the highest weighting and then direct the reviewer toa site if they are already logged onto that site. In that regards, thepossible destination sites may or may not be for reviews, ads, etc.

FIG. 1 illustrates an example of a conventional high-level architecture100 for optimizing dynamic review generation in accordance withimplementations of the disclosure. In some implementations, thearchitecture includes a central computer (hereinafter “the source system101”) that may include a memory device (not shown) storing a pluralityof software modules to implement dynamic review optimizer logic 160. Thedynamic review optimizer logic 160 may be, for example, a hardwarecomponent, circuitry, dedicated logic, programmable logic, microcode,etc., that may be implemented in a processing device (not shown) of thesource system 101.

The source system 101 may be communicatively connected with a merchantsystem 120, potential reviewers via reviewer client devices 130 (e.g., amobile phone), a plurality of impression sources (e.g., reviewing sites)150-1 through N, and a source system database 110. In oneimplementation, the software modules may be executed on one or morecomputer platforms of the source system 101 that are interconnected byone or more networks, which may include the Internet. Each of thereviewing sites 150-1 through N provides a plurality of modules (e.g.,APIs) with which the source system 101 interacts for carrying outoperations and providing relevant data between the source system 101,the merchant system 120 and the reviewer client devices 130.

In this example, the source system 101 allows merchants associated withthe merchant systems 120 to collect, analyze, and monitor consumerfeedback data (e.g., reviews/rating etc.) and other types of data from avariety of sources, including first-party reviewing websites (e.g., themerchant's own website or a service provider's website on the merchant'sbehalf), third-party reviewing websites (e.g., social networking andrating websites), and other sources. A first-party reviewing website150-1 may refer to a website maintained by the merchant that iscollecting reviews for themselves versus a third-party reviewing website150-2 through N, such as Google™, Facebook™, Yelp™, Citysearch™ and soon, that collect reviews of various merchants. In some implementation,an online forum may also be an example of other types of websites thatcan contain consumer feedback data (e.g., reviews) regarding a business.In one implementation, the reviewing sites 150-1 through N allowreviewers (e.g., via their client devices 130) to post reviews regardingall types of businesses.

In order to access the services of the source system 101 of FIG. 1, themerchant system 120 may register for an account with the source system101. In some implementations, merchants may access an API associatedwith the source system 101 to enter specific information that maycorrespond to their business profiles on reviewing sites 150-1 throughN. In other implementations, the merchants may access the source systemdirectly, a web platform or other means. This business profilesinformation 115, may be stored and include, for example, a businessname, address, telephone number, a link to a website for the business, apointer to a map of the location of the business, the business locationon a map, a promotional message for the business, and a list ofinformation regarding personal and product offerings of the business,which may include, but not limited to, menus, products services,upcoming and past events, etc. In other implementations, merchants 120may access pre-existing reviews from one or more First-Party ReviewSites and/or Third-Party Review Sites 150-1 through N. The access may bethrough one or more APIs, but also may be through scraping, JavaScriptor any other method of pulling data from such sites. The analytics anddata associated with these pre-existing reviews may be used as part ofthe algorithm implemented in the source system 101 as implemented by thedynamic review optimizer 160 described below.

A process of collecting consumer-feedback data (e.g., reviews) for amerchant may be initiated in several ways. For example, the sourcesystem 101 may transmit a review request 140 to the reviewer clientdevice 130 of a potential reviewer. In some implementations, the reviewrequest 140 may be transmitted via an electronic communication (althoughother transmission technologies are possible such as text messages,app-push notifications, including the request in a printed receiptreceived by the reviewer, add the request to an e-commerce cart, etc.).The source system 101 may receive a list of potential reviewers, forexample, from the merchant system 120. In some implementations, thislist may be received in response to detecting that the reviewer hasentered or exited a location associated with the merchant. For example,social networking sites allow reviewers to take actions such as“checking in” to a location. The merchant system 120 may detect thisaction and instruct the source system 101 to transmit a review request140 to the reviewer's client device 130. In other implementations, thereview request 140 may be sent to the reviewer's client device 130 basedon a purchase or browsing history by a potential reviewer regarding themerchant or other types of reviewer profile information collected by themerchant. In another implementation a merchant may manually select oneor more potential reviews and have the source system send reviewrequests to such selected potential reviewers.

The merchant system 120 may identify potential reviewers and when tosend review requests 140 to those reviewers. This may be activated soonafter the reviewer has had an experience at one of the locationsassociated with the merchant. For example, the reviewer may haveperformed a check out at a physical store location and then gets anemail at a determined time (e.g., 5, 10, 15 minutes) later requestingthe reviewer to review that store. In another example, review requests140 could be sent based on other factors involving time. In oneimplementation, a certain number of review requests 140 could be sent.If in a certain time frame, the reviews generated fall below a certainthreshold, additional review requests 140 could be sent at a later time.In another implementation, one could rate limit the review requests 140sent. In one implementation, the review request 140 may request thereviewer to provide a review of the physical location of the merchant.The source system 101 allows the merchant to observe the history relatedto the emailed review requests. For example, the request history may bestored in the system database 110 in a data table, such as in object115. In some implementations, the data table may include, but notlimited to, the following columns:

Column Contents Email The email address of the contact Name The firstand last name of the contact. Location Standard location information.Delivered The date time the email was delivered. If the email failed todeliver, instead this shall display an error icon and status. Statuses:Bounced Opened The date time the email was first opened. Clicked Thedate time the link in the email was first clicked. Site The site thereviewer was directed to when they clicked the link. Reviewed The datetime of the review attributed to this email. Last Action All, Delivered,Opened, Clicked, Reviewed Taken

The review request 140 may include a dynamic redirection container 145link for a reviewer to activate the reviewing process. For example, thedynamic redirection container 145 may automatically redirect thereviewer's client device to one site over another whether it is a firstparty reviewing site or a third party reviewing site. The dynamicredirection container 145 redirects the client device based on analgorithm implemented in the dynamic review optimizer logic 160, whichtakes into account several weighted event factors 155-1 through Nassociated with the reviewing sites 150-1 through N. In one embodiment,the event factors 155-1-N may be stored locally, for example, in a table115 of the source system database 110, In some embodiments, the table115 may be indexed by using and identifier for a particular 150-1through N, to identify corresponding event factors 155-1 through N forthat site. The dynamic review optimizer logic 160 is activated when areviewer clicks on the link indicating that they wish to provide areview. Then, all of the information associated with the weighted eventfactors 155-1 through N is analyzed to determine a redirection list 165of sites. This redirection list 165 may be used to determine to wherethe reviewer should be directed to provide the review. An advantage ofusing the dynamic redirection container 145 to redirect the clientdevice is that it allows different delivery mechanisms to be used totransmit the review request 140. For example, the dynamic container maybe transmitted via text message. In addition, the dynamic redirectioncontainer 145 allows the system to dynamically determine the site todirect reviewers based on the factors that may change from the time thelink was sent and at the time the reviewer clicks the link.

In other implementations other aspects of the review request 140 may bedynamically generated at various stages of the system and method. Forexample, an email, text message or other transmission technology may bedynamically generated instead of the dynamic redirection containeritself. In one embodiment, as soon as a reviewer has an experience at alocation associated with a merchant, an email may be automatically anddynamically generated based on an analysis of the plurality of weightedfactors. The email or other transmission technology may be adjustedbased on the Review Site picked to direct the reviewer to. For example,if Site A is chosen as the optimal review site, the email's branding ordesign may be created to match the branding or design of Site A. Inanother embodiment, a merchant may manually choose reviewers to send thereview request to. The email, text message or other communicationtechnology may be generated at this point dynamically. In otherembodiments, instead of a URL, frames, inline links, dynamic HTML,client-side scripting, AJAX, CSS or other means of providing access to aReview Site may be used to provide a reviewer with the ability to leavea review.

If the potential reviewer clicks the dynamic redirection container 145to post a review, the source system 101 may receive a selectionindicator. Thereupon, the source system 101 as directed by the dynamicreview optimizer logic 160 may pull in existing review statistics aboutthe merchant's location on a variety of sites. In anotherimplementation, these review statistics may have already been collectedby the dynamic review optimizer logic 160 that then evaluates oranalyzes such collected statistics at this time. Based on somedistribution parameters set up by the merchant system 120 and theirexisting distribution of reviews across those sites, the source system101 may dynamically decide which site to send that reviewer. This mayinclude a site associated with the source system 101 that collectsfirst-party reviews for the merchant.

A plurality of weighted event factors 155-1 through N associated withone or more sites 150-1 through N is analyzed based on the selection andinput from the merchant. In some implementations, a web crawler isprovided to crawl review sites and rating sites of the web. In thisregard, existing review statistics about the merchant's location arepulled from a variety of sites. The web crawler may identify links toother review sites and ratings sites of the data from the Internet. Insome implementation, all of the sites identified by the web crawler aremonitored. In other implementations, the merchant may choose which sitesthey want reviews to be monitored and analyzed. In one implementationpre-existing reviews are monitored from the one or more review sites150-1 through N through API connections. In other implementations, thisdata may be scraped, collected via JavaScript or any other mechanism andtools to collect review information from the review sites 150-1 throughN. Based on the analysis of the weighted event factors 155-1 through N,the dynamic review optimizer logic 160 redirects the client device to atleast one or the reviewing sites from the redirection list 145 for thereviewer to place a review of the merchant.

The weighted event factors 155-1 through N are considered in determiningwhich site 150-1 through N to direct the potential reviewer's device 130for placing a review. In some implementations, the merchant via themerchant's system 120 may adjust the weight given to any factor overanother factor. The merchant 120 may be provided with an interfacecontrol for the source system 101 to manipulate parameters for using anyof the event factors 155-1 through N used to determine which site todirect a potential reviewer. These adjustable event factors 155-1through N may include, but not limited to:

-   -   Knowledge about the potential reviewers    -   Location of the reviewing site    -   Amount of traffic at the reviewing site    -   Recency of the review    -   Statistical data regarding a particular reviewing site    -   External information regarding the merchant    -   Merchant reviewing site preferences    -   Other Factors

Knowledge about the Potential Reviewers

In this example, information collected about specific reviewersassociated with the reviewer client device 130 may also be used. Forexample, the frequency for which a specific reviewer leaves reviews onspecific sites may be considered. If a user leaves reviews on Site Amore than Site B, (or is a super user of Site A) the merchant may wantto send the reviewer to site A. Additionally, knowledge of a particularuser's affiliation with a specific site may be relevant. For example, ifit is known that a reviewer is already logged into Site B, that reviewermay be more likely to leave a review on Site B than another site. Inanother example, if a reviewer is known to leave fake or otherwiseobjectionable reviews, that user may be flagged. In some instances, theemail wouldn't send to such reviewer if the algorithm determines thatthe reviewer leaves fake reviews.

The information about the potential reviewers may also include reviewerprofile information received from the reviewer client device 130. Insome implementations, this reviewer profile information may indicatecertain factors regarding the reviewer that makes it more likely thatthe reviewer may use one site of the sites 150-1 through N over anothersite. This may indicate how likely the potential reviewer is to completea review at a particular site, for example, based on past reviews postedby the reviewer at that site.

Location of the Reviewing Site

With regards to this factor, the location of the reviewing site 150-1though N may refer to sites that the merchant expects consumers todiscover them at. These can be popular sites or sites directed to asubject matter associated with the merchant.

The Amount of Traffic at the Reviewing Site

The amount of traffic at a particular one of the reviewing sites 150-1though N may refer to a threshold number (set by the merchant) ofcustomers that must visit a particular reviewing site before themerchant wants review request to be directed to that site. This mayindicate a site where customers frequently seek information about themerchants. In some implementations, the traffic to the reviewing sitemay be below a certain threshold. For example, the merchant on one sitemay have 1,000 reviews with an average rating of 3.6, and on anothersite the merchant may have 10 reviews with an average is 2.4. In thisexample, the system may send more reviewing traffic to the site with 10reviews in order to raise the aggregate rating for that site.

Recency of the Review

The recency of the last review on a particular one of the reviewingsites 150-1 though N may refer to a merchant's desire to have aconcentration of new reviews at a particular reviewing cite to optimizea rating for the merchant at that site. In some implementations, themerchant may indicate its desire to make sure certain reviewing siteshave the most recent reviews to scale reviews at the site from aconsumer point of view. In some implementations, the merchant can definethe most recent reviews as reviews occurring in the last month or someother type of time indicator. In one illustrative example, if a numberof reviewers are being sent to other sites for various reasons and aparticular site has not received a review in the last month, the systemmay want to send all recent reviewers to the site.

Statistical Data Regarding a Particular Reviewing Site

Statistical data regarding the reviewing sites 150-1 through N may beused as a factor in determining which site to direct potentialreviewers. For example, a merchant may have received a rating ofthree-and-a-half (3.5) stars at a certain website and two-and-a-half(2.5) stars at other sites. The dynamic review optimizer may analyzewhere consumers are leaving reviews, and determine an aggregate ratingof the merchant across the sites. The dynamic review optimizer may thendetermine a statistical deviation level based on the data and determineif the ratings on a particular site is outside of a normal level. Inthis example, more reviewers may be directed to a particular sitebecause if enough reviewers are sent there the rating may raise to anormal level regardless of whether the reviews are positive or negative.For example, if out of 1,000 reviews on one site the average is 3.5, butthere are only 10 reviews on the other site with the 2.5 average, bybuilding up the reviewers to 1,000 people this may raise the overallaggregate rating on that site to 3.5 like in the other site.

In some implementations, the dynamic review optimizer logic 160 mayreduce the generation of review requests 140 for the site with morereviews to test the impact of the reviews on the aggregate rating forthe merchant on that site. In this regard, the dynamic review optimizermay use data signals adjust the rating across multiple sites. Forexample, the merchant may want a 50/50 split of reviews across multiplereviewing sites. Over time the system may adjust the direction of thereviews based on other factors like the entry mechanism for reviews onone site may be easier than another. Thus, the source system 101 mayintuitively learn from the data signals to adjust the split that sendsreviewers to a site to generate desirable ratio of reviews across sites.

External Information Regarding the Merchant

Any outlying external factor regarding the merchant may be used as afactor to determining which of the sites 150-1 through N to directpotential reviewers. For example, if there is some change at themerchant's location that would cause one review site to have lowreviews, such as a change in a health code score from an “A” to a “C”for the merchant. This information may be retrieved, for example, from aratings database from the Department of Health or other sources. Thedynamic review optimizer may predict how this external information isgoing to impact ratings. In such a situation, traffic may be directed tothe merchant's first party reviewing site rather than a third partyreviewing site to better understand how the company is being presentedduring the time the merchant's location is impacted. In othersituations, requests for reviews may not be set at all so as to notadversely impact the Merchant's ratings.

Merchant Reviewing Site Preferences

In some implementations, the merchant may indicate their desire forreviews to be on a particular site of the plurality of sites 150-1through N. As such, the distribution of reviews may be adjusted so thatcertain site receives more reviews than others. In some situations, themerchant may prefer for the reviews to be on their first-party siterather than a third-party site because on the third party sites themerchant has less control on how reviews are filtered. In somesituations, the merchant may want to push low rating reviews off of afront page of a reviewing website by pushing more positive reviews. Forexample, it is common with reviewing sites to show a number of reviews(e.g., five) and then there is a paging function to click to the nextpage to see more reviews. Some merchants may have a preference that ifthere is a one star review in the last five reviews, the dynamic reviewoptimizer may send more people to the reviewing site so as to push thatone-star review to the second or latter page. Thus, the higher ratedreviews are the first thing someone sees if they go to that reviewingwebsite.

In one illustrative example, the merchant may configure the dynamicreview optimizer logic 165 for certain reviewing sites and apply aweighting for each site. First, the merchant may add a number of sitesfor sending review requests and then adjust a ratio for each site. Insome implementations, the merchant may choose one or more optionsrelated to this ratio, such as avoid 1-star reviews in the five mostrecent first party reviews or matching logged in reviewers to sites. Themerchant may then save these options so that the dynamic reviewoptimizer may use the options to decide on how to route reviews when areviewer clicks on a link associated with a review request.

In some implementations, the method and system may also be used togenerate review requests 140 for other types of information instead ofjust reviews. For example, a merchant may be able to use a dynamicredirection container 145 or other mechanism to dynamically direct acustomer to a website to check to ensure if the location data of suchmerchant is correct on such site. In another example, the techniques ofthe present disclosure may dynamically send a customer to alocation-specific site based on a variety of weighted factors. Inanother implementation, instead of generating a request, a merchant maybe able to use the weighted factors to generate an email specific to alocation of a merchant most directly applicable to such customer.

FIG. 2 illustrates is an example system 200 including memory 204 foroptimizing dynamic review generation in accordance with one or moreaspects of the disclosure. The system 200 may be executed on one or morecomputer platforms interconnected by one or more networks, which mayinclude the Internet. In some embodiments, memory 204 may a systemdatabase (such as database 110), or a storage system comprising thesystem database. A central computer platform (hereinafter “the sourcesystem 201 a”) includes a system database 204 and a plurality ofsoftware modules 210, 220, 230, 240, 250 and 260 that arecommunicatively connected with one or more merchants 203, and aplurality of service provider computer platforms (hereinafter “theservice provider system(s) 201 b”).

Each of the service provider computer platforms 201 b provides aplurality of modules 270, 280 with which the source system 201 ainteracts for carrying out operations and providing provider storedreviews data 280 between the source system 201 a and the serviceprovider computer platforms 201 b regarding posted reviews from, forexample, a potential reviewer 207 at a physical merchant location 215.Although reviewer 207 is shown in FIG. 2 at the merchant location 215,the reviewer 207 may be at another location not associated with themerchant 203 when providing reviews about the merchant 203.

The system 200 allows the merchants 203 to collect, analyze, and monitorconsumer feedback data (e.g., reviews/rating etc.) and other types ofdata from a variety of sources including the service provider computerplatforms 201 b (e.g., the merchant's own website or a serviceprovider's website on the merchant's behalf) to achieve an optimaldistribution of reviews across the numerous reviewing sites. The sourcesystem 201 a may receive a list of potential reviewers, such as reviewer207. In some implementations, this list may be received in response todetecting that the reviewer 207 has entered or exited the merchantlocation 215. For example, social networking sites allow reviewers totake actions such as “checking in” to a location. In otherimplementations, the list of potential reviewers may be based on apurchase or browsing history by the reviewer 207 regarding the merchant203 or other types of reviewer profile information collected by themerchant 203. In another implementation, the merchant 203 may manuallyselect one or more potential reviews for the source system 201 a to sendreview requests to such to reviewer 207.

To access the services of the system 200, the merchant 203 may beprovided with an operator web application 210. The operator webapplication 210 allows the merchant 203 to review, develop and adjust analgorithm used for determining where the reviewer 207 should be directedto provide the review. In this regard, the operator web application 210may allow the merchant 203 to input several types of distributionparameters for optimizing the distribution of reviews. Thesedistribution parameters may include, for example, weighted valuesassigned to certain reviewing sites, a number of review requests to sendduring a given time period, timing for the review requests, a targetdistribution of reviews to store at each site, recency of the reviews aswell as other distribution parameters to balance and optimize adistribution of the reviews. Further aspects of the operator webapplication 210 as discussed with respect to FIGS. 4 and 5.

With respect to FIG. 2, the system 200 includes a review generator 220.The review generator 220 may implement the dynamic review optimizerlogic 160 of FIG. 1. The dynamic review generator 220 takes into accountseveral weighted event factors associated with the reviewing sites ofthe service provider computer platforms 201 b and the distributionparameters specified by the merchant 203 via the operator webapplication 210. Based on this information, the dynamic review generator220 generates a dynamic redirection container 235 link for a reviewer207 to activate the reviewing process. For example, the dynamicredirection container 235 redirection container 235 may automaticallyredirect the reviewer's 207 client device to one site over another siteassociated with the service provider computer platforms 201 b.

The dynamic review generator 220 may instruct the review transmitter 230to transmit the dynamic redirection container 235 to reviewer 207 viatheir client device. The review transmitter 230 may transmit the dynamicredirection container 235 in, for example, a review request usingseveral types of techniques. For example, the dynamic redirectioncontainer 235 may be transmitted via an electronic communication, suchas a text message, email, app-push notifications, etc. In otherimplementations, the dynamic redirection container 235 may betransmitted in a printed receipt received by the reviewer 207 or addedto an online cart, etc.

If the reviewer 207 clicks on or enter the dynamic redirection container235 into their client device to post a review, the source system 201 amay receive a selection indicator 217. This selection indicator 217 mayinclude relevant data regarding the reviewer 235, such as browsinghistory, location, purchasing history as well as other relevantinformation of the reviewer 235. The source system 201 a may theninstruct the weighted event engine 240 to pull in existing reviewstatistics about the merchant's location on a variety of sitesassociated with the service provider computer platforms 201 b. Inanother implementation, the weighted event engine 240 may have alreadybeen collected review statistics from the sites of the provider computerplatforms 201 b then evaluates or analyzes such collected statistics. Inthis regard, the weighted event engine 240 may access specific providerapplication program interfaces (APIs) 270 to collected review statisticsregarding provider reviews 280 stored at the service provider computerplatforms 201 b. In another implementation, the review statistics may bestored in the source system 201 a database 204 or another database.

The weighted event engine 240 elevates the collected review statisticsand information from the review generator regarding the merchant'sspecified distribution parameters to identify or otherwise produce aplurality of weighted event factors for each of the sites in the serviceprovider computer platforms 201 b. The weighted event factors relate toa cross section of the reviewing sites to ensure that the reviewsreflect the most current consumer sentiment about the merchant 203. Inaccordance with the weighted event factors, the weighted event engine240 redirects the reviewer's client device to at least one site of sitesAPI 270 to be perceived by the reviewer 207 as being associated with thedynamic redirection container 235. In some implementations, the weightedevent engine 240 may produce a redirection list that lists the potentialsites, URLs, or other network address to redirect the reviewer's clientdevice. This redirection list may be stored, for example, in a databaseof memory 204. Based on the weighted event factors for each site, theweighted event engine 240 may then select a site view the provider APIsto redirect the reviewer 207 so that they can place a review at thatsite associated with the service provider computer platforms 201 b.

Several different weighted event factors may be used to determine wherethe reviewer's client device will be redirected. For example, if theweighted event factor for a particular site in below a certain thresholdvalue, the reviewer's client device may be redirected to that site(e.g., to a provider API 270) so that reviews on the site can meet thethreshold. This threshold value may represent, for example, a targetdistribution of reviews that the merchant' desires to be stored at thesite based on present reviews stored thereon. In some implementations, aclient device of the reviewer 207 may be redirected to a particular siteif it is determined that the weighted event factor that site meets arecency of reviews for the merchant stored thereon. In someimplementations, if it is determined that the weighted event factor forthe site is below a target review rating for the merchant, the reviewer207 may be redirected to the site. In other implementations, if it isdetermined that the weighted event factor for the site meets a priorityindicator specified by the merchant 203 in the distribution parameters,the reviewer 207 may be redirected to the site to place a review. Thismay indicate the merchant's 203 preferences for reviews to be placed ata certain site over other sites associated with the service providercomputer platforms 201 b.

Once a review is generated through the dynamic redirection container 235that review may be pulled back into the source system 201 a so that thedynamic review monitor 250 can monitor it with other reviews currentlybeing monitored. For example, the dynamic review monitor 250 mayretrieve the provider stored review data 280 via the provider API 270.The review data left by the reviewer 207 may be located based onidentifying information associated with the reviewer 207 that wastransmitted to the source system 201 a via the selection indicator 217.In other implementations, the review data may include a specific flag ormarker when it is generated based on the dynamic redirection container235 transmitted to the client device 207.

Once the reviews are “flagged” or otherwise tagged to indicate that thereviews ordinated from the dynamic review generation dynamic URL, thedynamic review monitor 250 can then either automatically or manuallyupdate the review generator 220 based on how the monitored reviews aremeeting the desired optimization (e.g., distribution parametersspecified by the merchant 203. For example, the dynamic review monitor250 may transmit weight adjustments 255 or otherwise update the weightedevent factors generated by the weighed event engine to take into accountthe monitored reviews. For example, the weight adjustments 255 mayincrease or decrease the weighted value given to certain weighted eventfactors.

In some implementations, the source system 201 a may detect if thereviewer 207 is already logged into a particular reviewing site, such asthe merchant's site or a third party reviewing site. For example, thelogin verifier 260 may access the provider API(s) 270 to determinewhether the reviewer 207 is currently logged in. In some embodiments,the login verifier 260 may examine the browsing history of the reviewer207 received in the selection indicator 217 to determine whether thereviewer 207 is logged into or has recently logged into a particularsite. At this point, the login verifier 260 may instruct the reviewgenerator 220 to use the weighted event factors to select sites with thehighest weighting and then redirect the reviewer 207 via the dynamicredirection container 235 to a particular site if they are alreadylogged onto that site.

FIG. 3 illustrates an example interface portal 300 to support optimizingdynamic review generation for redirecting request in accordance with oneor more aspects of the disclosure. In some implementations, theinterface portal 300 may correspond to a portal of the operator Webapplication 210 of FIG. 2. The interface portal 300 provides an exampleinterface in which merchants, such as merchant 203, can develop andadjust an algorithm used for determining where the reviewer, such asreviewer 207, should be directed to provide the review. In this regard,the operator web application 210 may allow the merchant to input severaltypes of distribution parameters for optimizing the distribution ofreviews. These distribution parameters may include, for example,weighted values assigned to certain reviewing sites, a number of reviewrequests to send during a given time period, timing for the reviewrequests, a target distribution of reviews to store at each site,recency of the reviews as well as other distribution parameters tobalance and optimize a distribution of the reviews.

In some implementations, the interface portal 300 may include severalpanels that include a review generation panel 310, an algorithm settingspanel 320 and a general settings panel 330. The review generation panel310 displays the target distribution 312 of reviews for the merchant bysite 314. This may include an assigned weight value 316 given to eachsite as specified by the merchant. For example, the weight 318 may be avalue between a certain range (e.g., 1-5) where lower value weights aregiven less preference for distributing reviews to than to higher valueweighted sites. The algorithm settings panel 320 allows the merchant toconfigure the algorithm 322 and balancing optimization of settings usedfor the targeted distribution 312. The general settings 330 allows themerchant to set distribution parameters which include, but not limitedto, a maximum number of review request to send 332, enabling 1^(st)party (e.g., merchant) submission pages 334, quarantine new 1^(st) partyreviews 336 and override privacy policy rules 338.

With respect to FIG. 4, an example dashboard interface 400 is shown. Insome implementations, the interface portal 300 may correspond to aportal of the operator Web application 210 of FIG. 2. The dashboardinterface 400 provides an example interface in which merchants, such asmerchant 203, can review the results of generating reviews to solvevarious problems specified by the merchant, such as merchant 203, foroptimizing the distribution of reviews. In some implementations, thedashboard interface 400 may display certain results, such as top sitesdistribution graph 410 for specified sites 420, displaying the listingswith low ratings 430, listings with no recent reviews 440, and locationswith recent 1-star first party reviews 450. Based on the results, themerchants can update settings for the generated rating distribution 470.In some implementations, the dashboard interface 400 may also providethe merchants with way to monitor reviews generated through the dynamicredirection container 235. For example, the reviews may be pulled backinto the source system 201 a and displayed in a generated review panel480 of the dashboard interface 400. Advantages of the generated reviewpanel 480 is that is allows the merchant to monitor certain reviews 490along with other reviews generated through the dynamic redirectioncontainer 235.

FIG. 5 illustrates a flow diagram of a method 500 in accordance with oneor more aspects of the disclosure. In one implementation, processingdevice 201 of FIG. 2 as executed by the dynamic review optimizer logic160 of FIG. 1 may perform method 500 to optimize dynamic reviewgeneration for redirecting request links. The method 500 may beperformed by processing logic that may comprise hardware (circuitry,dedicated logic, etc.), software (such as is run on a general purposecomputer system or a dedicated machine), or a combination of both.Alternatively, in some other implementations, one or more processors ofthe computer device executing the method may perform routines,subroutines, or operations may perform method 500 and each of itsindividual functions. In certain implementations, a single processingthread may perform method 500. Alternatively, two or more processingthreads with each thread executing one or more individual functions,routines, subroutines, or operations may perform method 500. It shouldbe noted that blocks of method 500 depicted in FIG. 5 can be performedsimultaneously or in a different order than that depicted.

Referring to FIG. 5, at block 510, method 500 creates a dynamicredirection container 145 for an online review request 140 for a reviewof a merchant 205. In block 520, the dynamic redirection container 145is transmitted to a client device of a user 207 at the physical location215. A confirmation 217 is received from the client device 130 in block530 that the dynamic redirection container is activated by the user. Inblock 540, one or more weighted event factors 155-1 through N associatedwith a plurality of sites 150-1 through N are identified based on thereceived confirmation 217 and distribution parameters 300 specified by amerchant system 120 for the merchant 205. In block 550, the clientdevice 130 is redirected, in accordance with the weighted event factors155-1 through N, to at least one site of the plurality sites 150-1through N as being associated with the dynamic URL 230.

FIG. 6 illustrates a flow diagram of a method 600 for in accordance withone or more aspects of the disclosure. In one implementation, processingdevice 201 of FIG. 2 as executed by the dynamic review optimizer logic160 of FIG. 1 may perform method 600 to modify optimized dynamic reviewgeneration for redirecting request links. The method 600 may beperformed by processing logic that may comprise hardware (circuitry,dedicated logic, etc.), software (such as is run on a general purposecomputer system or a dedicated machine), or a combination of both.Alternatively, in some other implementations, one or more processors ofthe computer device executing the method may perform routines,subroutines, or operations may perform method 600 and each of itsindividual functions. In certain implementations, a single processingthread may perform method 600. Alternatively, two or more processingthreads with each thread executing one or more individual functions,routines, subroutines, or operations may perform method 600. It shouldbe noted that blocks of method 600 depicted in FIG. 6 can be performedsimultaneously or in a different order than that depicted.

Referring to FIG. 6, at block 610, method 600 receives a confirmationindicator 217 from a client device 130 based on a dynamic redirectioncontainer 145 transmitted to the client device 130, the dynamicredirection container 145 is associated with a request 140 for an onlinereview of a merchant 205. In block 620, one or more weighted eventfactors 155-1 through N associated with a plurality of sites 150-1through N are identified based on the confirmation indicator 137 anddistribution parameters 300 specified by the merchant 205. In block 620,the client device 130 redirect, in accordance with the weighted eventfactors 155-1 through N, to at least one site of the plurality sites150-1 through N to be perceived by a user 207 as being associated withthe dynamic redirection container 145. In block 620, the weighted eventfactors 155-1 through N are updated based on review data 260 for themerchant 205 posted by the redirected client device 130.

FIG. 7 illustrates a flow diagram of a method 700 in accordance with oneor more aspects of the disclosure. In one implementation, processingdevice 201 of FIG. 2 as executed by the dynamic review optimizer logic160 of FIG. 1 may perform method 700 to optimize dynamic reviewgeneration for redirecting request links based on user logininformation. The method 700 may be performed by processing logic thatmay comprise hardware (circuitry, dedicated logic, etc.), software (suchas is run on a general purpose computer system or a dedicated machine),or a combination of both. Alternatively, in some other implementations,one or more processors of the computer device executing the method mayperform routines, subroutines, or operations may perform method 700 andeach of its individual functions. In certain implementations, a singleprocessing thread may perform method 700. Alternatively, two or moreprocessing threads with each thread executing one or more individualfunctions, routines, subroutines, or operations may perform method 700.It should be noted that blocks of method 700 depicted in FIG. 7 can beperformed simultaneously or in a different order than that depicted.

Referring to FIG. 7, at block 710, method 700 receives a confirmationindicator 217 from a client device 130 based on a dynamic redirectioncontainer 145 transmitted to the client device 130. In block 720, one ormore weighted event factors 155-1 through N associated with a pluralityof sites 150-1 through N are identified based on the confirmationindicator 217 and a set of input parameters 320-340 specified by themerchant 205. It is determined in block 730 that a user 207 associatedwith the client device 130 is logged onto one or more sites 290 of theplurality of sites 150-1 through N. In block 740, the client device 130,based on the weighted event factors 155-1 through N, is redirected to atleast one of the logged onto sites associated with the dynamicredirection container 145.

FIG. 8 illustrates an example computer system 800 operating inaccordance with some embodiments of the disclosure. In FIG. 8, adiagrammatic representation of a machine is shown in the exemplary formof the computer system 800 within which a set of instructions, forcausing the machine to perform any one or more of the methodologiesdiscussed herein, may be executed. In alternative embodiments, themachine 800 may be connected (e.g., networked) to other machines in alocal area network (LAN), an intranet, an extranet, or the Internet. Themachine 800 may operate in the capacity of a server or a client machinein a client-server network environment, or as a peer machine in apeer-to-peer (or distributed) network environment. The machine may be apersonal computer (PC), a tablet PC, a set-top box (STB), a personaldigital assistant (PDA), a cellular telephone, a web appliance, aserver, a network router, switch or bridge, or any machine capable ofexecuting a set of instructions (sequential or otherwise) that specifyactions to be taken by that machine 800. Further, while only a singlemachine is illustrated, the term “machine” shall also be taken toinclude any collection of machines that individually or jointly executea set (or multiple sets) of instructions to perform any one or more ofthe methodologies discussed herein.

The Example computer system 800 may comprise a processing device 802(also referred to as a processor or CPU), a main memory 804 (e.g.,read-only memory (ROM), flash memory, dynamic random access memory(DRAM) such as synchronous DRAM (SDRAM), etc.), a static memory 806(e.g., flash memory, static random access memory (SRAM), etc.), and asecondary memory (e.g., a data storage device 816), which maycommunicate with each other via a bus 830.

Processing device 802 represents one or more general-purpose processingdevices such as a microprocessor, central processing unit, or the like.More particularly, the processing device may be complex instruction setcomputing (CISC) microprocessor, reduced instruction set computer (RISC)microprocessor, very long instruction word (VLIW) microprocessor, orprocessor implementing other instruction sets, or processorsimplementing a combination of instruction sets. Processing device 802may also be one or more special-purpose processing devices such as anapplication specific integrated circuit (ASIC), a field programmablegate array (FPGA), a digital signal processor (DSP), network processor,or the like. Processing device 802 is configured to execute dynamicreview optimizer logic 160 for performing the operations and stepsdiscussed herein. For example, the processing device 802 may beconfigured to execute instructions implementing method 500, method 600and 700, for optimizing dynamic review generation for redirectingrequest links, in accordance with one or more aspects of the disclosure.

Example computer system 800 may further comprise a network interfacedevice 822 that may be communicatively coupled to a network 825. Examplecomputer system 800 may further comprise a video display 810 (e.g., aliquid crystal display (LCD), a touch screen, or a cathode ray tube(CRT)), an alphanumeric input device 812 (e.g., a keyboard), a cursorcontrol device 814 (e.g., a mouse), and an acoustic signal generationdevice 820 (e.g., a speaker).

Data storage device 816 may include a computer-readable storage medium(or more specifically a non-transitory computer-readable storage medium)824 on which is stored one or more sets of executable instructions 826.In accordance with one or more aspects of the present disclosure,executable instructions 826 may comprise executable instructionsencoding various functions of the dynamic review optimizer logic 160 inaccordance with one or more aspects of the present disclosure.

Executable instructions 826 may also reside, completely or at leastpartially, within main memory 804 and/or within processing device 802during execution thereof by example computer system 800, main memory 804and processing device 802 also constituting computer-readable storagemedia. Executable instructions 826 may further be transmitted orreceived over a network via network interface device 822.

While computer-readable storage medium 824 is shown as a single medium,the term “computer-readable storage medium” should be taken to include asingle medium or multiple media. The term “computer-readable storagemedium” shall also be taken to include any medium that is capable ofstoring or encoding a set of instructions for execution by the machinethat cause the machine to perform any one or more of the methodsdescribed herein. The term “computer-readable storage medium” shallaccordingly be taken to include, but not be limited to, solid-statememories, and optical and magnetic media.

Some portions of the detailed descriptions above are presented in termsof algorithms and symbolic representations of operations on data bitswithin a computer memory. These algorithmic descriptions andrepresentations are the means used by those skilled in the dataprocessing arts to most effectively convey the substance of their workto others skilled in the art. An algorithm is here, and generally,conceived to be a self-consistent sequence of steps leading to a desiredresult. The steps are those requiring physical manipulations of physicalquantities. Usually, though not necessarily, these quantities take theform of electrical or magnetic signals capable of being stored,transferred, combined, compared, and otherwise manipulated. It hasproven convenient at times, principally for reasons of common usage, torefer to these signals as bits, values, elements, symbols, characters,terms, numbers, or the like.

It should be borne in mind, however, that all of these and similar termsare to be associated with the appropriate physical quantities and aremerely convenient labels applied to these quantities. Unlessspecifically stated otherwise, as apparent from the followingdiscussion, it is appreciated that throughout the description,discussions utilizing terms such as “identifying,” “determining,”“creating,” “transmitting,” “receiving,” “producing,” “redirecting,”“updating,” “generating” or the like, refer to the action and processesof a computer system, or similar electronic computing device, thatmanipulates and transforms data represented as physical (electronic)quantities within the computer system's registers and memories intoother data similarly represented as physical quantities within thecomputer system memories or registers or other such information storage,transmission or display devices.

Examples of the present disclosure also relate to an apparatus forperforming the methods described herein. This apparatus may be speciallyconstructed for the required purposes, or it may be a general-purposecomputer system selectively programmed by a computer program stored inthe computer system. Such a computer program may be stored in a computerreadable storage medium, such as, but not limited to, any type of diskincluding optical disks, CD-ROMs, and magnetic-optical disks, read-onlymemories (ROMs), random access memories (RAMs), EPROMs, EEPROMs,magnetic disk storage media, optical storage media, flash memorydevices, other type of machine-accessible storage media, or any type ofmedia suitable for storing electronic instructions, each coupled to acomputer system bus.

The methods and displays presented herein are not inherently related toany particular computer or other apparatus. Various general-purposesystems may be used with programs in accordance with the teachingsherein, or it may prove convenient to construct a more specializedapparatus to perform the required method steps. The required structurefor a variety of these systems will appear as set forth in thedescription below. In addition, the scope of the present disclosure isnot limited to any particular programming language. It will beappreciated that a variety of programming languages may be used toimplement the teachings of the present disclosure.

It is to be understood that the above description is intended to beillustrative, and not restrictive. Many other implementation exampleswill be apparent to those of skill in the art upon reading andunderstanding the above description. Although the present disclosuredescribes specific examples, it will be recognized that the systems andmethods of the present disclosure are not limited to the examplesdescribed herein, but may be practiced with modifications within thescope of the appended claims. Accordingly, the specification anddrawings are to be regarded in an illustrative sense rather than arestrictive sense. The scope of the present disclosure should,therefore, be determined with reference to the appended claims, alongwith the full scope of equivalents to which such claims are entitled.

What is claimed is:
 1. A computer-implemented method comprising:identifying, by a processing device of a computing system, a user deviceassociated with a user to provide a review of a merchant; identifying,by the computing system, a weighted event factor associated at least aportion of a plurality of websites to which the review can beredirected; determining, by the computing system, based on the weightedevent factor, a first website of the plurality of websites to which toredirect the user device; transmitting, via a computer network, adynamic redirection container that redirects the client device to thefirst website; receiving, via the dynamic redirection container from theuser device, a posted review of the merchant at the first website; andupdating, based on the posted review associated with the merchant, theweighted event factor.
 2. The method of claim 1, further comprisingdetermining the user device is located at a location associated with themerchant.
 3. The method of claim 1, further comprising detecting atransaction, executed using the user device, associated with themerchant.
 4. The method of claim 1, further comprising identifying abrowsing history of the user device that is associated with themerchant.
 5. The method of claim 1, further comprising determining theweighted event factor associated with the first website is below athreshold value.
 6. The method of claim 5, wherein the weighted eventfactor comprises a current amount of reviews associated with themerchant that are posted at the first website.
 7. The method of claim 1,wherein the weighted event factor comprises a first priority indicatorassociated with the first website of the plurality of websites; andwherein the first priority indicator is greater than a second priorityindicator associate with a second website of the plurality of websites.8. A system comprising: a memory to store instructions; and a processingdevice operatively coupled to the memory, the processing device toexecute the instructions to perform operations comprising: identifying auser device associated with a user to provide a review of a merchant;identifying a weighted event factor associated at least a portion of aplurality of websites to which the review can be redirected;determining, based on the weighted event factor, a first website of theplurality of websites to which to redirect the user device;transmitting, via a computer network, a dynamic redirection containerthat redirects the client device to the first website, wherein thedynamic redirection container receives a posted review of the merchantat the first website; and updating, based on the posted reviewassociated with the merchant, the weighted event factor.
 9. The systemof claim 8, the operations further comprising determining the userdevice is located at a location associated with the merchant.
 10. Thesystem of claim 8, the operations further comprising detecting atransaction, executed using the user device, associated with themerchant.
 11. The system of claim 8, the operations further comprisingidentifying a browsing history of the user device that is associatedwith the merchant.
 12. The system of claim 8, the operations furthercomprising determining the weighted event factor associated with thefirst website is below a threshold value.
 13. The system of claim 12,wherein the weighted event factor comprises a current amount of reviewsassociated with the merchant that are posted at the first website. 14.The system of claim 8, wherein the weighted event factor comprises afirst priority indicator associated with the first website of theplurality of websites; and wherein the first priority indicator isgreater than a second priority indicator associate with a second websiteof the plurality of websites.
 15. A non-transitory computer readablestorage medium having instructions that, when executed by a processingdevice, cause the processing device to perform operations comprising:identifying a user device associated with a user to provide a review ofa merchant; identifying a weighted event factor associated at least aportion of a plurality of websites to which the review can beredirected; determining, based on the weighted event factor, a firstwebsite of the plurality of websites to which to redirect the userdevice; transmitting, via a computer network, a dynamic redirectioncontainer that redirects the client device to the first website, whereinthe dynamic redirection container receives a posted review of themerchant at the first website; and updating, based on the posted reviewassociated with the merchant, the weighted event factor.
 16. Thenon-transitory computer readable storage medium of claim 15, theoperations further comprising determining the user device is located ata location associated with the merchant.
 17. The non-transitory computerreadable storage medium of claim 15, the operations further comprisingdetecting a transaction, executed using the user device, associated withthe merchant.
 18. The non-transitory computer readable storage medium ofclaim 15, the operations further comprising identifying a browsinghistory of the user device that is associated with the merchant.
 19. Thenon-transitory computer readable storage medium of claim 15, theoperations further comprising determining the weighted event factorassociated with the first website is below a threshold value.
 20. Thenon-transitory computer readable storage medium of claim 19, wherein theweighted event factor comprises a current amount of reviews associatedwith the merchant that are posted at the first website.